Negative libertyNegative liberty is freedom from interference by other people. Negative liberty is primarily concerned with freedom from external restraint and contrasts with positive liberty (the possession of the power and resources to fulfill one's own potential). The distinction originated with Bentham, was popularized by T.H. Green and Guido De Ruggiero, and is now best known through Isaiah Berlin's 1958 lecture "Two Concepts of Liberty". Stanford Encyclopedia of Philosophy describes negative liberty: "The negative concept of freedom .
Search algorithmIn computer science, a search algorithm is an algorithm designed to solve a search problem. Search algorithms work to retrieve information stored within particular data structure, or calculated in the search space of a problem domain, with either discrete or continuous values. Although search engines use search algorithms, they belong to the study of information retrieval, not algorithmics. The appropriate search algorithm to use often depends on the data structure being searched, and may also include prior knowledge about the data.
Negative and positive rightsThe right of person A to obligate (enforce an obligation on) person B to refrain from (causal) physical interference with, in particular a purely interfering negligence tort against, some object or thing is called a negative right. So a negative right is a claim right. If a claim right is not a negative right, it is called a positive right. To every claim right of person A to obligate person B corresponds the obligation on B, so the obligation corresponding to a negative right is called a 'negative obligation' and an obligation corresponding to positive right a 'positive obligation'.
Check digitA check digit is a form of redundancy check used for error detection on identification numbers, such as bank account numbers, which are used in an application where they will at least sometimes be input manually. It is analogous to a binary parity bit used to check for errors in computer-generated data. It consists of one or more digits (or letters) computed by an algorithm from the other digits (or letters) in the sequence input.
Markov decision processIn mathematics, a Markov decision process (MDP) is a discrete-time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via dynamic programming. MDPs were known at least as early as the 1950s; a core body of research on Markov decision processes resulted from Ronald Howard's 1960 book, Dynamic Programming and Markov Processes.
Stochastic gradient descentStochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated from the entire data set) by an estimate thereof (calculated from a randomly selected subset of the data).
